Synthetic data.
We adopted 4 widely used synthetic datasets in order to evaluate the effectivness of our method, namely DSprites [1]}, Shapes3D [2]}, Cars3D [3]}, SmallNORB [4]} . All these datasets contain images that are parametrically rendered based on some known factors of variation, the goal is thus to obtain a disentangled latent space that reflects these parameters. We resized the input images to a dimension of \(64 \times 64\)  pixels. During training we give as input to our method two images which differ just for a single randomly sampled factor of variation, and this is the only assumption that we make on the input data. Note that we do not provide any information on which of the factors is changing between the two images.
redWe show quantitative results on these datasets in Tables REF ,REF ,REF ,REF  comparing our method to the state-of-the-art model Ada-GVAE of [5]}, in terms of the metrics described in the relative section.
